Privacy-preserving federated learning: Strengthening data protection and secure collaboration in healthcare
Abstract
Machine learning (ML) has been an integral part of large-scale data analysis in healthcare research and is being used for tasks such as disease classification, pattern recognition, or synthetic data generation. ML models learn the values of numerous parameters that allow them to capture patterns and relationships within a training dataset. It is essential to validate medical ML models in multi-centre studies, which test their performance on a diverse and heterogeneous patient population, proving a good generalisability and the absence of biases. However, due to its sensitive nature, sharing and centrally aggregating medical data is strictly regulated, for instance under the European General Data Protection Regulation (GDPR). It often requires complex pseudonymisation or anonymisation steps, which introduce a procedural overhead and may require removing relevant patient information in favour of privacy preservation. Federated learning (FL) addresses the challenge of sensitive data sharing by decentralising the training step of ML models, thereby removing the need to transmit data off-site. Training is organised in a client-server architecture, where data owners take on the role of clients, and a so-called parameter server is responsible for maintaining the latest global model, facilitating the training procedure, and aggregating local training progress. In multiple training rounds, the clients receive the model from the parameter server and train it on their local data,…
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